Overview

Dataset statistics

Number of variables9
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows88
Duplicate rows (%)0.9%
Total size in memory781.2 KiB
Average record size in memory80.0 B

Variable types

Categorical7
Text2

Dataset

Description팀별 등록정보(팀명, 종목, 소속구분, 소속시도 등)
Author대한체육회
URLhttps://www.data.go.kr/data/15052697/fileData.do

Alerts

Dataset has 88 (0.9%) duplicate rowsDuplicates
등록년도 is highly overall correlated with 소속세부구분High correlation
소속세부구분 is highly overall correlated with 등록년도High correlation
종별구분 is highly imbalanced (92.2%)Imbalance
세부종목구분 is highly imbalanced (96.2%)Imbalance
소속구분 is highly imbalanced (82.3%)Imbalance

Reproduction

Analysis started2023-12-12 23:29:57.079156
Analysis finished2023-12-12 23:29:58.730380
Duration1.65 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

등록년도
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2007
1623 
2010
1127 
2011
1118 
2019
1071 
2004
813 
Other values (22)
4248 

Length

Max length4
Median length4
Mean length3.9991
Min length1

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row2010
2nd row2007
3rd row2012
4th row2010
5th row2004

Common Values

ValueCountFrequency (%)
2007 1623
16.2%
2010 1127
11.3%
2011 1118
11.2%
2019 1071
10.7%
2004 813
8.1%
2013 572
 
5.7%
2012 451
 
4.5%
2009 447
 
4.5%
2014 402
 
4.0%
2006 390
 
3.9%
Other values (17) 1986
19.9%

Length

2023-12-13T08:29:58.833272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2007 1623
16.2%
2010 1127
11.3%
2011 1118
11.2%
2019 1071
10.7%
2004 813
8.1%
2013 572
 
5.7%
2012 451
 
4.5%
2009 447
 
4.5%
2014 402
 
4.0%
2006 390
 
3.9%
Other values (17) 1986
19.9%

팀명
Text

Distinct7509
Distinct (%)75.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T08:29:59.068276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length35
Mean length6.4806
Min length1

Characters and Unicode

Total characters64806
Distinct characters639
Distinct categories13 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5815 ?
Unique (%)58.1%

Sample

1st row봉천여자중학교
2nd row봉은삼산초등학교
3rd row충렬중학교
4th row가평중학교
5th row논산농업고등학교
ValueCountFrequency (%)
과거팀 69
 
0.7%
북구 21
 
0.2%
달성 11
 
0.1%
달서 10
 
0.1%
경기 9
 
0.1%
우석대학교 8
 
0.1%
없음 8
 
0.1%
2관 8
 
0.1%
무소속 8
 
0.1%
체육회 7
 
0.1%
Other values (7708) 10406
98.5%
2023-12-13T08:29:59.575513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
학 7350
 
11.3%
교 7255
 
11.2%
등 4530
 
7.0%
초 3334
 
5.1%
중 2589
 
4.0%
고 1624
 
2.5%
대 1377
 
2.1%
산 906
 
1.4%
여 751
 
1.2%
천 746
 
1.2%
Other values (629) 34344
53.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 63028
97.3%
Space Separator 567
 
0.9%
Uppercase Letter 550
 
0.8%
Lowercase Letter 176
 
0.3%
Open Punctuation 149
 
0.2%
Close Punctuation 149
 
0.2%
Decimal Number 115
 
0.2%
Dash Punctuation 35
 
0.1%
Other Punctuation 22
 
< 0.1%
Connector Punctuation 12
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
학 7350
 
11.7%
교 7255
 
11.5%
등 4530
 
7.2%
초 3334
 
5.3%
중 2589
 
4.1%
고 1624
 
2.6%
대 1377
 
2.2%
산 906
 
1.4%
여 751
 
1.2%
천 746
 
1.2%
Other values (559) 32566
51.7%
Uppercase Letter
ValueCountFrequency (%)
M 65
 
11.8%
C 54
 
9.8%
A 45
 
8.2%
F 41
 
7.5%
S 40
 
7.3%
K 32
 
5.8%
B 29
 
5.3%
U 28
 
5.1%
G 26
 
4.7%
Y 22
 
4.0%
Other values (15) 168
30.5%
Lowercase Letter
ValueCountFrequency (%)
o 19
 
10.8%
e 16
 
9.1%
t 14
 
8.0%
i 13
 
7.4%
a 12
 
6.8%
s 11
 
6.2%
c 10
 
5.7%
n 10
 
5.7%
l 10
 
5.7%
h 9
 
5.1%
Other values (13) 52
29.5%
Decimal Number
ValueCountFrequency (%)
1 40
34.8%
2 32
27.8%
5 13
 
11.3%
3 9
 
7.8%
9 6
 
5.2%
4 5
 
4.3%
0 4
 
3.5%
8 3
 
2.6%
6 2
 
1.7%
7 1
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 15
68.2%
& 5
 
22.7%
; 1
 
4.5%
# 1
 
4.5%
Space Separator
ValueCountFrequency (%)
567
100.0%
Open Punctuation
ValueCountFrequency (%)
( 149
100.0%
Close Punctuation
ValueCountFrequency (%)
) 149
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 35
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 12
100.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 1
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 63028
97.3%
Common 1051
 
1.6%
Latin 726
 
1.1%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
학 7350
 
11.7%
교 7255
 
11.5%
등 4530
 
7.2%
초 3334
 
5.3%
중 2589
 
4.1%
고 1624
 
2.6%
대 1377
 
2.2%
산 906
 
1.4%
여 751
 
1.2%
천 746
 
1.2%
Other values (559) 32566
51.7%
Latin
ValueCountFrequency (%)
M 65
 
9.0%
C 54
 
7.4%
A 45
 
6.2%
F 41
 
5.6%
S 40
 
5.5%
K 32
 
4.4%
B 29
 
4.0%
U 28
 
3.9%
G 26
 
3.6%
Y 22
 
3.0%
Other values (38) 344
47.4%
Common
ValueCountFrequency (%)
567
53.9%
( 149
 
14.2%
) 149
 
14.2%
1 40
 
3.8%
- 35
 
3.3%
2 32
 
3.0%
. 15
 
1.4%
5 13
 
1.2%
_ 12
 
1.1%
3 9
 
0.9%
Other values (11) 30
 
2.9%
Han
ValueCountFrequency (%)
孝 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 63027
97.3%
ASCII 1776
 
2.7%
Punctuation 1
 
< 0.1%
None 1
 
< 0.1%
CJK 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
학 7350
 
11.7%
교 7255
 
11.5%
등 4530
 
7.2%
초 3334
 
5.3%
중 2589
 
4.1%
고 1624
 
2.6%
대 1377
 
2.2%
산 906
 
1.4%
여 751
 
1.2%
천 746
 
1.2%
Other values (558) 32565
51.7%
ASCII
ValueCountFrequency (%)
567
31.9%
( 149
 
8.4%
) 149
 
8.4%
M 65
 
3.7%
C 54
 
3.0%
A 45
 
2.5%
F 41
 
2.3%
1 40
 
2.3%
S 40
 
2.3%
- 35
 
2.0%
Other values (58) 591
33.3%
Punctuation
ValueCountFrequency (%)
‘ 1
100.0%
None
ValueCountFrequency (%)
㈜ 1
100.0%
CJK
ValueCountFrequency (%)
孝 1
100.0%

종별
Categorical

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
초등부
3510 
중학부
2580 
고등부
1679 
실업(일반)
799 
대학부
621 
Other values (4)
811 

Length

Max length10
Median length3
Mean length3.5207
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row중학부
2nd row초등부
3rd row중학부
4th row중학부
5th row고등부

Common Values

ValueCountFrequency (%)
초등부 3510
35.1%
중학부 2580
25.8%
고등부 1679
16.8%
실업(일반) 799
 
8.0%
대학부 621
 
6.2%
기타(일반) 434
 
4.3%
시도군청(삭제예정) 244
 
2.4%
- 111
 
1.1%
군,경찰 22
 
0.2%

Length

2023-12-13T08:29:59.745100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:29:59.874283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
초등부 3510
35.1%
중학부 2580
25.8%
고등부 1679
16.8%
실업(일반 799
 
8.0%
대학부 621
 
6.2%
기타(일반 434
 
4.3%
시도군청(삭제예정 244
 
2.4%
111
 
1.1%
군,경찰 22
 
0.2%

종별구분
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
-
9777 
경기단체
 
71
시도청
 
67
시도체육회
 
40
기업
 
33

Length

Max length5
Median length1
Mean length1.0576
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 9777
97.8%
경기단체 71
 
0.7%
시도청 67
 
0.7%
시도체육회 40
 
0.4%
기업 33
 
0.3%
공공기관 12
 
0.1%

Length

2023-12-13T08:30:00.025883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:30:00.179262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
9777
97.8%
경기단체 71
 
0.7%
시도청 67
 
0.7%
시도체육회 40
 
0.4%
기업 33
 
0.3%
공공기관 12
 
0.1%

종목
Text

Distinct61
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T08:30:00.390740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length2
Mean length2.4508
Min length2

Characters and Unicode

Total characters24508
Distinct characters119
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row농구
2nd row육상
3rd row수영
4th row검도
5th row배구
ValueCountFrequency (%)
육상 3342
33.4%
골프 540
 
5.4%
체조 377
 
3.8%
수영 364
 
3.6%
복싱 349
 
3.5%
빙상 284
 
2.8%
보디빌딩 270
 
2.7%
유도 264
 
2.6%
검도 231
 
2.3%
배구 214
 
2.1%
Other values (51) 3765
37.6%
2023-12-13T08:30:00.709361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
상 3716
 
15.2%
육 3342
 
13.6%
스 990
 
4.0%
도 804
 
3.3%
구 712
 
2.9%
프 649
 
2.6%
싱 587
 
2.4%
수 556
 
2.3%
복 542
 
2.2%
골 540
 
2.2%
Other values (109) 12070
49.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 24099
98.3%
Decimal Number 144
 
0.6%
Other Punctuation 90
 
0.4%
Open Punctuation 62
 
0.3%
Close Punctuation 62
 
0.3%
Connector Punctuation 51
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 3716
 
15.4%
육 3342
 
13.9%
스 990
 
4.1%
도 804
 
3.3%
구 712
 
3.0%
프 649
 
2.7%
싱 587
 
2.4%
수 556
 
2.3%
복 542
 
2.2%
골 540
 
2.2%
Other values (103) 11661
48.4%
Decimal Number
ValueCountFrequency (%)
5 100
69.4%
3 44
30.6%
Other Punctuation
ValueCountFrequency (%)
· 90
100.0%
Open Punctuation
ValueCountFrequency (%)
( 62
100.0%
Close Punctuation
ValueCountFrequency (%)
) 62
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 51
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 24099
98.3%
Common 409
 
1.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 3716
 
15.4%
육 3342
 
13.9%
스 990
 
4.1%
도 804
 
3.3%
구 712
 
3.0%
프 649
 
2.7%
싱 587
 
2.4%
수 556
 
2.3%
복 542
 
2.2%
골 540
 
2.2%
Other values (103) 11661
48.4%
Common
ValueCountFrequency (%)
5 100
24.4%
· 90
22.0%
( 62
15.2%
) 62
15.2%
_ 51
12.5%
3 44
10.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 24099
98.3%
ASCII 319
 
1.3%
None 90
 
0.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
상 3716
 
15.4%
육 3342
 
13.9%
스 990
 
4.1%
도 804
 
3.3%
구 712
 
3.0%
프 649
 
2.7%
싱 587
 
2.4%
수 556
 
2.3%
복 542
 
2.2%
골 540
 
2.2%
Other values (103) 11661
48.4%
ASCII
ValueCountFrequency (%)
5 100
31.3%
( 62
19.4%
) 62
19.4%
_ 51
16.0%
3 44
13.8%
None
ValueCountFrequency (%)
· 90
100.0%

세부종목구분
Categorical

IMBALANCE 

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
-
9835 
리커브
 
61
에어로빅
 
30
단체전
 
26
개인전
 
11
Other values (17)
 
37

Length

Max length10
Median length1
Mean length1.0391
Min length1

Unique

Unique10 ?
Unique (%)0.1%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 9835
98.4%
리커브 61
 
0.6%
에어로빅 30
 
0.3%
단체전 26
 
0.3%
개인전 11
 
0.1%
컴파운드 10
 
0.1%
스피드 4
 
< 0.1%
경영 3
 
< 0.1%
기계체조 3
 
< 0.1%
컬링 3
 
< 0.1%
Other values (12) 14
 
0.1%

Length

2023-12-13T08:30:01.064172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9835
98.3%
리커브 61
 
0.6%
에어로빅 30
 
0.3%
단체전 26
 
0.3%
개인전 11
 
0.1%
컴파운드 10
 
0.1%
스피드 4
 
< 0.1%
경영 3
 
< 0.1%
기계체조 3
 
< 0.1%
컬링 3
 
< 0.1%
Other values (13) 16
 
0.2%

소속구분
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
엘리트
9734 
동호인
 
266

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row엘리트
2nd row엘리트
3rd row엘리트
4th row엘리트
5th row엘리트

Common Values

ValueCountFrequency (%)
엘리트 9734
97.3%
동호인 266
 
2.7%

Length

2023-12-13T08:30:01.158084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:30:01.233884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
엘리트 9734
97.3%
동호인 266
 
2.7%

소속세부구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
-
5837 
운동부(학교,직장)
2282 
클럽,체육관 등
1881 

Length

Max length10
Median length1
Mean length4.3705
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-
2nd row-
3rd row운동부(학교,직장)
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 5837
58.4%
운동부(학교,직장) 2282
 
22.8%
클럽,체육관 등 1881
 
18.8%

Length

2023-12-13T08:30:01.334914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:30:01.427649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5837
49.1%
운동부(학교,직장 2282
 
19.2%
클럽,체육관 1881
 
15.8%
등 1881
 
15.8%

시도
Categorical

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경기
1704 
서울
1469 
경북
686 
경남
661 
강원
599 
Other values (14)
4881 

Length

Max length4
Median length2
Mean length2.0018
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울
2nd row충북
3rd row부산
4th row경기
5th row충남

Common Values

ValueCountFrequency (%)
경기 1704
17.0%
서울 1469
14.7%
경북 686
 
6.9%
경남 661
 
6.6%
강원 599
 
6.0%
전북 579
 
5.8%
충남 566
 
5.7%
부산 549
 
5.5%
대구 506
 
5.1%
전남 474
 
4.7%
Other values (9) 2207
22.1%

Length

2023-12-13T08:30:01.531577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 1704
17.0%
서울 1469
14.7%
경북 686
 
6.9%
경남 661
 
6.6%
강원 599
 
6.0%
전북 579
 
5.8%
충남 566
 
5.7%
부산 549
 
5.5%
대구 506
 
5.1%
전남 474
 
4.7%
Other values (9) 2207
22.1%

Correlations

2023-12-13T08:30:01.614276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록년도종별종별구분종목세부종목구분소속구분소속세부구분시도
등록년도1.0000.6800.2270.7800.2050.1160.7790.376
종별0.6801.0000.4270.6530.0690.2040.4870.477
종별구분0.2270.4271.0000.3860.2860.0000.3660.079
종목0.7800.6530.3861.0000.8200.5330.9000.533
세부종목구분0.2050.0690.2860.8201.0000.0000.2470.000
소속구분0.1160.2040.0000.5330.0001.0000.0730.108
소속세부구분0.7790.4870.3660.9000.2470.0731.0000.237
시도0.3760.4770.0790.5330.0000.1080.2371.000
2023-12-13T08:30:01.734800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록년도종별구분종별세부종목구분소속구분시도소속세부구분
등록년도1.0000.1010.2980.0550.1000.1110.518
종별구분0.1011.0000.2280.1350.0000.0370.163
종별0.2980.2281.0000.0270.2030.2080.246
세부종목구분0.0550.1350.0271.0000.0000.0000.131
소속구분0.1000.0000.2030.0001.0000.0950.121
시도0.1110.0370.2080.0000.0951.0000.128
소속세부구분0.5180.1630.2460.1310.1210.1281.000
2023-12-13T08:30:01.833436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록년도종별종별구분세부종목구분소속구분소속세부구분시도
등록년도1.0000.2980.1010.0550.1000.5180.111
종별0.2981.0000.2280.0270.2030.2460.208
종별구분0.1010.2281.0000.1350.0000.1630.037
세부종목구분0.0550.0270.1351.0000.0000.1310.000
소속구분0.1000.2030.0000.0001.0000.1210.095
소속세부구분0.5180.2460.1630.1310.1211.0000.128
시도0.1110.2080.0370.0000.0950.1281.000

Missing values

2023-12-13T08:29:58.454332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:29:58.642547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

등록년도팀명종별종별구분종목세부종목구분소속구분소속세부구분시도
739462010봉천여자중학교중학부-농구-엘리트-서울
454462007봉은삼산초등학교초등부-육상-엘리트-충북
49792012충렬중학교중학부-수영-엘리트운동부(학교,직장)부산
590352010가평중학교중학부-검도-엘리트-경기
735152004논산농업고등학교고등부-배구-엘리트-충남
86252004인천시청시도군청(삭제예정)-역도-엘리트-인천
661242015대불대학교대학부-농구-엘리트운동부(학교,직장)전남
854562011홍먹초등학교초등부-육상-엘리트-충북
426432010세종대학교대학부-육상-엘리트-서울
469332009잠실고등학교고등부-육상-엘리트-서울
등록년도팀명종별종별구분종목세부종목구분소속구분소속세부구분시도
180842014안동 격투기초등부-킥복싱-엘리트클럽,체육관 등경북
399102010소양서초등학교초등부-육상-엘리트-전북
564722019완도군청실업(일반)시도청보디빌딩-엘리트운동부(학교,직장)전남
515212009궁근정초등학교초등부-육상-엘리트-울산
735182011서울신목초등학교초등부-바둑-엘리트클럽,체육관 등서울
919662012중동고등학교고등부-농구-엘리트운동부(학교,직장)서울
380222015우암초등학교초등부-롤러스피드엘리트운동부(학교,직장)경남
894532015서초권투체육관중학부-복싱-동호인-서울
380452007경상남도바둑협회중학부-바둑-엘리트-경남
258692013중경OB산악회기타(일반)-산악-엘리트클럽,체육관 등대전

Duplicate rows

Most frequently occurring

등록년도팀명종별종별구분종목세부종목구분소속구분소속세부구분시도# duplicates
82P007금곡고등학교--유도-엘리트-경기3
02004서울성산중중학부-체조-엘리트-서울2
12004양운고등학교고등부-펜싱-엘리트운동부(학교,직장)부산2
22005계명문화대학교대학부-스쿼시-엘리트-대구2
32006과거팀고등부-농구-엘리트-서울2
42007가회중학교중학부-육상-엘리트-경남2
52007거성초등학교초등부-육상-엘리트-강원2
62007거창교육청시도군청(삭제예정)-육상-엘리트-경남2
72007거학초등학교초등부-육상-엘리트-부산2
82007경포고등학교고등부-육상-엘리트-강원2